Prosecution Insights
Last updated: April 19, 2026
Application No. 18/072,533

AUTOMATION OF GENERATING ROBOTIC PROCESS AUTOMATION FROM AUTOMATION DOMAIN SPECIFIC LANGUAGES

Non-Final OA §101§102§112
Filed
Nov 30, 2022
Examiner
KENDALL, CHUCK O
Art Unit
2192
Tech Center
2100 — Computer Architecture & Software
Assignee
International Business Machines Corporation
OA Round
1 (Non-Final)
87%
Grant Probability
Favorable
1-2
OA Rounds
3y 1m
To Grant
95%
With Interview

Examiner Intelligence

Grants 87% — above average
87%
Career Allow Rate
796 granted / 914 resolved
+32.1% vs TC avg
Moderate +8% lift
Without
With
+7.7%
Interview Lift
resolved cases with interview
Typical timeline
3y 1m
Avg Prosecution
24 currently pending
Career history
938
Total Applications
across all art units

Statute-Specific Performance

§101
11.9%
-28.1% vs TC avg
§103
22.8%
-17.2% vs TC avg
§102
52.3%
+12.3% vs TC avg
§112
3.3%
-36.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 914 resolved cases

Office Action

§101 §102 §112
Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This in reference to Application filed 11/30/22. Claims have been examined and are pending. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 6 and 20 recites the limitation "the process automation requirements supplemental technology” " in line 2. There is insufficient antecedent basis for this limitation in the claim. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because based on abstract idea. Paragraphs [0002]-[0004] of the specification disclose steps recited in claim 1 were done by “RPA developers”, i.e., automation of manual process. See MPEP 2106.05(a). Example claim 1: “receiving ...” (Step 2A, Prong 1, collecting data). “generating ...”, “generating”, and “building ...” (Step 2A, Prong 2, merely applying the judicial exception). “deploying ...” (Step 2A, Prong 2, insignificant extra solution activity). “processor set”, “robotic process automation code”, “robotic process automation robot”, and “production environment” are recited at a high level of generality. Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. Claim(s) 1 – 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cella et al. 20220187847 A1. Regarding claims 1, 14 and 17, a method/a computer program product comprising one or more computer readable storage media (non-transitory described in paragraph 0025 of spec)/ system, comprising: receiving, by a processor set, process automation requirements specifying a process flow [0028, see “…received job content to identify candidate portions thereof for robot automation.… that facilitates robot selection and task ordering in a workflow of robot tasks…”]; generating, by the processor set, alternative automation requirements in a domain specific language from terms of the domain specific language identified in the process automation requirements [0017 - 0018, see “…actions include domain-specific actions that are responsive to the respective requests… proposed fleet configurations given a set of tasks to be completed by a robot fleet… suitable for robot automation”] and regarding alternative see 0043, which discloses, “…digital thread may include multiple alternative such instruction sets…”] also see [1214, shows translating between first and second languages and domain specific taxonomies]. generating, by the processor set, robotic process automation code from the alternative automation requirements in the domain specific language [0043, see “digital thread constitutes information related to the complete lifecycle of the part from design, modeling, production, validation, …a digital thread may include a set of instructions, …digital thread (i.e. automation code) may include multiple alternative such instruction sets ,” also refer to 0018 which specifies this is for robot automation]; building, by the processor set, a robotic process automation robot deployable in a production environment using the robotic process automation code (see 0018,… a set of one or more processors that execute a set of computer-readable instructions), also see [0021”… workflow system generates (i.e. build) a workflow that defines an order of performance of the robot tasks based on the fleet resource configuration data structure and the set of robot tasks (i.e. robotic automation)”] also see [0267, “…configured to provide a set of capabilities that facilitate development and deployment of intelligence, such as for facilitating automation..”]; and deploying, by the processor set, the robotic process automation robot in the production environment[0267, shows facilitating deployment]. Regarding claim 2, the method of claim 1, further comprising identifying, by the processor set, the terms of the domain specific language from the process automation requirements using an artificial intelligence model trained to identify the terms of the domain specific language by comparing features extracted from text in the process automation requirements with features of terms extracted from text in a corpus of training data from robotic process automation projects [0029 see, “…the fleet intelligence layer facilitates sending portions of the job content identified as suitable for robot automation to a machine learning service of the set of intelligence services for improving job content parsing…” also see 0016, which shows the artificial intelligence determines based on analysis and defining and determining actions ]. Regarding claim 3, the method of claim 1, further comprising: identifying, by the processor set, requirements in the process automation requirements with actions based on satisfying conditions for performing the actions expressed in the process automation requirements using an artificial intelligence model trained to identify the conditions [0029, see requirements and constraints] expressed for performing the actions in the process automation requirements by comparing features extracted from text in the process automation requirements with features extracted from text specifying other actions based on satisfying other conditions for performing the other actions expressed in a corpus of training data from robotic process automation projects [0029 see, “…the fleet intelligence layer facilitates sending portions of the job content identified as suitable for robot automation to a machine learning]; and creating conditional operators in the domain specific language for the identified requirements with the actions based on satisfying the conditions for performing the actions expressed in the process automation requirements [0136, see conditional probabilities]. Regarding claim 4, the method of claim 1, further comprising generating, by the processor set, alternative automation requirements in the domain specific language with conditional operators for requirements in the process automation requirements with actions based on satisfying conditions for performing the actions expressed in the process automation requirements [0043, which discloses, “…digital thread may include multiple alternative such instruction sets…”] also see [1214, shows translating between first and second languages and domain specific taxonomies]. Regarding claim 5, the method of claim 1, further comprising identifying, by the processor set, the robotic process automation code from the alternative automation requirements in the domain specific language using a machine learning model employing a Long Short Term Memory (LSTM) algorithm trained with features of a plurality of process automation requirements and a plurality of robotic process automation code associated with the features of the plurality of process automation requirements from training data of robotic process automation projects [1519, see LTSM]. Regarding claim 6, the method of claim 1, further comprising appending, by the processor set, to keywords from the process automation requirements supplemental terminology selected from the group consisting of an alternative reference, search names, and an association to commands [0043, see alternative]. Regarding claim 7, the method of claim 1, further comprising storing, by the processor set, the alternative automation requirements in the domain specific language in persistent storage [0043]. Regarding claim 8, the method of claim 1, further comprising storing, by the processor set, the robotic process automation code in persistent storage [0267, storage]. Regarding claim 9, the method of claim 1, further comprising inserting, by the processor set, the generated robotic process automation code into a robotic process automation template [1069, see template]. Regarding claim 10, the method of claim 1, further comprising inserting, by the processor set, the generated robotic process automation code into a robotic process automation script [0018, see robot automation]. Regarding claim 11, the method of claim 1, wherein the process automation requirements are selected from the group consisting of a process definition document, a business process modeling notation diagram, and an audio file of spoken process automation requirements [1037 - 1056, shows audio processing and flow diagram]. Regarding claim 12, the method of claim 2, wherein the training data is based on keywords and programmed actions [0270]. Regarding claim 13, the method of claim 5, wherein the training data is based on keywords and programmed actions [0270]. Regarding claim 15 and 18. The computer program product of claim 14, wherein the program instructions are further executable to create conditional operators in the domain specific language for requirements with actions based on satisfying conditions for performing the actions expressed in the requirements [0043]. Regarding claim 16 and 19, the computer program product of claim 14 wherein the program instructions are further executable to identify the terms of the domain specific language from the received process automation requirements using an artificial intelligence model trained to identify the terms of the domain specific language from a corpus of training data of robotic process automation projects [0043 – 0048]. Regarding claim 20, the system of claim 17, wherein the program instructions are further executable to append to keywords from the process automation requirements supplemental terminology selected from the group consisting of an alternative reference, search names, and an association to commands [0043 – 0048]. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Marinkovich US 12511630 B2 teaches similarly with regards to robotic automation and domain specific languages. Correspondence Information Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.” Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chuck Kendall whose telephone number is 571-272-3698. The examiner can normally be reached on 10:00 am - 6:30pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung Sough can be reached on 571-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). /Chuck O Kendall/ Primary Examiner, Art Unit 2192
Read full office action

Prosecution Timeline

Nov 30, 2022
Application Filed
Nov 09, 2023
Response after Non-Final Action
Jan 07, 2026
Non-Final Rejection — §101, §102, §112
Mar 24, 2026
Interview Requested

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
87%
Grant Probability
95%
With Interview (+7.7%)
3y 1m
Median Time to Grant
Low
PTA Risk
Based on 914 resolved cases by this examiner. Grant probability derived from career allow rate.

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